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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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multicellular immune response against the tumor. Chimeric antigen receptor (CAR) T cell therapies have transformed the treatment of B cell malignancies, yet their broader implementation is constrained by complex
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e.g., ultra-cold gases of bosonic or fermionic atoms, machine learning technologies and quantum computing. At the same time, we work in close connection with IJCLab experimentalists, particularly
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benjamin.devauchelle@pasteur.fr in Cc. The candidate should have a PhD in Human Genetics and the following skills: Strong interest in the field of genetics, neurobiology and psychiatry. Computer skills: strong level in
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processing and analysis tools, programming (Python, Origin, Octave) and the mathematical and computer tools necessary to exploit the results • Written and oral English (level C1/C2) in a multicultural work
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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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molecular dynamics simulations of modified nucleosomes - analyze the large data set obtained using various analysis tools, from visualization to automation using machine learning tools - perform QM/MM
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or by car (parking available). Public transportation costs are partially covered. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR7104-MARMEN-001/Default.aspx Requirements Research
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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production tasks toward a role of regulator, coordinator, or even supervisor of the flexible production machine system. In the first phase, our objective is to capitalize on the information we have gathered